基于垂直约束的激光扫描机构外参标定算法

熊峰; 刘成菊; 陈启军

doi:10.16383/j.aas.c190264

基于垂直约束的激光扫描机构外参标定算法

doi: 10.16383/j.aas.c190264

1.
同济大学电子与信息工程学院控制科学与工程系上海 201804

基金项目: 国家自然科学基金重点项目(61573260, 61733013), 上海市科学技术委员会项目(18DZ1200804, 18DZ1206803)资助

详细信息

作者简介:
熊峰：同济大学电子与信息工程学院博士研究生. 2014年获得同济大学电子与信息工程学院学士学位. 主要研究方向为3D立体视觉, 机器人导航. E-mail: wozhiailuo@foxmail.com

刘成菊：同济大学电子与信息工程学院教授. 2011年获得同济大学控制科学与工程系博士学位. 主要研究方向为双足机器人行走控制. 本文通信作者. E-mail: liuchengju@tongji.edu.cn

陈启军：同济大学电子与信息工程学院教授. 1999年获得同济大学电气工程系博士学位. 主要研究方向为机器人与人工智能. E-mail: qjchen@tongji.edu.cn

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出版历程
- 收稿日期: 2019-03-29
- 录用日期: 2019-09-24
- 网络出版日期: 2021-05-21
- 刊出日期: 2021-05-20

The External Calibration Algorithm for Plane Laser-Scanning Machanism Based on Vertical Constraint

1.
Control Science and Engineering, College of Electronic and Information Engineering, Tongji University, Shanghai 201804

Funds: Supported by National Natural Science Foundation of China (61573260, 61733013) and Science and Technology Commission of Shanghai Municipality (18DZ1200804, 18DZ1206803)

More Information

Author Bio:
XIONG Feng　Ph.D. candidate at the College of Electronic and Information Engineering, Tongji University. He received his bachelor degree from Tongji University in 2014. His research interest covers 3D stereovision and robot navigation

LIU Cheng-Ju　Professor at the College of Electronic and Information Engineering, Tongji University. She received her Ph.D. degree in control science and engineering from Tongji University in 2011. Her main research interest is biped robot walking control. Corresponding author of this paper

CHEN Qi-Jun　Professor at the College of Electronic and Information Engineering, Tongji University. He received his Ph.D. degree in electrical engineering from Tongji University in 1999. His research interest covers robotics and artificial intelligence

摘要

摘要:
为了解决人造特征点标定法中特征匹配不精确等缺陷, 本文针对二轴传动的高精度平面激光扫描机构提出了利用线特征的垂直约束进行外参标定的新算法. 不仅如此, 在该算法中为了简化建立标定方程的流程, 避免计算与标定目标无关的冗余中间量, 提出了一种快速确定标定方程参数的方法. 首先将扫描结果按待标定参数标准值转换至同一坐标系形成点云, 再提取其中的线特征; 接着根据线特征的垂直约束建立外参方程, 并根据线特征的测量值和实际值间的转换计算方程参数; 最终, 将多次测量得到的方程组求解转化为最优化问题, 并得到外参的数值解. 在对比实验中, 本算法比基于特征点的标定方法表现更好.
- 激光扫描 /
- 点云 /
- 外参标定 /
- 最优化
Abstract:
In order to solve the defect of inaccurate feature matching in the artificial feature point calibration method, a new algorithm for the external parameter calibration using the vertical constraint of line feature for the plane laser-scanning machanism of high precision two-axis transmission is proposed in this article. Moreover, in this algorithm, in order to simplify the process of establishing the calibration equation and avoid calculating the redundant intermediate quantity unrelated to the calibration target, a method for quickly determining the parameters of the calibration equation is proposed. Firstly, the scan result is converted to the same coordinate system according to the standard value of the parameter to be calibrated to form a point cloud, and then the line features are extracted; then the external parameter equation is established according to the vertical constraint of the line feature, and the parameters of the equation is calculated according to the conversion between the measured value and the actual value of the line feature; finally, the equations obtained from the multiple measurements are transformed into optimization problems, and the numerical solution of the external parameters is obtained. In the contrast experiment, the algorithm performs better than the feature point based calibration method.
- Laser scan /
- point cloud /
- external calibration /
- optimization

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图 1 激光探头与平面扫描机构

Fig. 1 Laser and plane scaning mechanism

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图 2 棋盘标定板与特征点缺失

Fig. 2 Chess calibration board and lack of feature points

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图 3 圆斑标定板与扫描结果的形状畸变

Fig. 3 Spot calibration board and shape distortion of scanning result

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图 4 实际扫描机构

Fig. 4 Real scanning mechanism

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图 5 标定板扫描与线特征提取过程

Fig. 5 Scanning of calibration board and procedure of line feature extraction

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图 6 标定板旋转

Fig. 6 Rotation of calibration board

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图 7 距离校验

Fig. 7 Distance check

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图 8 使用棋盘格标定及扫描结果

Fig. 8 Calibration with chessboard and scanning result

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表 1 测量结果

Table 1 Measurement results

直线 1 的方向向量	直线 2 的方向向量
$[0.99659, 0.03356,0.07539]$	$[-0.04385,0.99904,0.00192]$
$[-0.69945,0.71395,-0.03244]$	$[0.71329,-0.03317,0.70008]$
$[0.80461,0.58967,0.06998]$	$[-0.58046,0.81253,-0.05343]$
$[-0.61523,0.78755,-0.03541]$	$[0.78759,0.61540,-0.03129]$
$[0.79372,0.59944,0.10337]$	$[-0.59260,0.80175,-0.07760]$
$[0.87606,-0.47478,0.08426]$	$[0.46701,0.88316,0.04391]$
$[0.82421,-0.56561,0.02783]$	$[0.56293,0.82555,-0.03971]$

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表 2 算法比较

Table 2 Comparision between algorithms

比较指标	基于角点	基于圆心	本文算法
指标 1)	否 (需单应性矩阵)	否 (需坐标变换)	是
指标 2)	否	是	否
指标 3)	无 (算法难以实践)	0.94 %	0.15 %

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